AI BriefingA Case Study of LLM-as-a-Judge-based RAG Evaluation
Key point
This covers a case study of building an LLM-as-a-Judge evaluation system to ensure the reliability of a RAG chatbot for agricultural support.
Details
Digital Green and CGIAR developed Farmer.chat, a RAG-based chatbot, to provide accurate agricultural information to smallholder farmers. The system aims to deliver reliable advice based on a vast collection of agricultural research papers.
The key components of the system architecture are as follows:
- Knowledge Base: Research papers are processed using Semantic Chunking and stored in QdrantDB to optimize retrieval.
- RAG Pipeline: Relevant information is retrieved from the vector DB and passed to the LLM to generate grounded answers.
- User-facing Agent: Based on GPT-4o, it uses ReAct prompting to identify user intent and perform step-by-step reasoning for tool calls.
To validate the system's performance in a complex environment that must account for diverse languages and regional contexts, the team adopted an LLM-as-a-judge approach to build an evaluation system that assesses the accuracy and reliability of RAG outputs.
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